Bryan is a computer scientist who develops methods rooted in machine learning and optimization to improve decision making in public health. His PhD work at Harvard University focused on HIV prevention and tuberculosis treatment and led to a deployed intervention for HIV prevention among youth experiencing homelessness.
As a Schmidt Science Fellow working in the Chan Lab at Harvard School of Public Health, Bryan dived more deeply into epidemiology and global health, using large administrative and cohort datasets to understand the roots of health disparities and discover effective points of intervention.
He is now an Assistant Professor in the Machine Learning Department at Carnegie Mellon University.
Bryan is motivated to work across computer science and public health with the ultimate goal of improving health for marginalized communities. In the future, Bryan hopes both to advance the science of data-driven decision making and to deploy and evaluate systems which improve health in practice.